Track 15: Biomedical Signal Analysis with Machine Intelligence
Biomedical Signal Analysis with Machine Intelligence
One area of interest for researchers among the many uses of machine learning in biomedical engineering is its application in biomedical signal processing, which involves extracting, analysing, and classifying different signals or images for diagnostic reasons. The majority of the time, nonlinear and changing environments are used to obtain biological signals. The processing and categorization of bioelectric signals, such as an electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), etc., can also be successfully accomplished with machine learning. Three steps make up the processing of an ECG signal: feature extraction, classification, and preprocessing (filtering).
In order to effectively treat patients, this chapter focuses on the deployment of machine learning-based algorithms at different stages of an ECG signal’s processing to extract information that is helpful for the early and accurate detection of cardiac diseases.
Artificial Intelligence (AI) and the Internet of Medical Things (IoMT) can now monitor the course of disease almost continuously thanks to smart biomedical signal processing tools, increasing patient knowledge of their diseases. AI has typically been applied to cloud computing following the transfer of sensor data from the bedside. AI-computing is becoming more and more possible before it reaches the cloud because to sensor capabilities, customised algorithms, and lightweight processing. Relocating AI from the cloud to the edge of the data pipeline can improve patient data security and ease bandwidth concerns, therefore optimising IoMT for patient care. Multimodality in bio-signals combined with machine intelligence can increase knowledge of human physiology, health, and the course of disease (among other things).
Reduced interobserver variability, dose adjustments for oocyte stimulation, fewer in-person medical encounters, which boosts user and medical productivity, improved sperm sample selection, assessment of oocyte quality, and embryo selection are just a few of the many benefits that an AI ART software can offer.
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